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<!DOCTYPE html>
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<head>
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<title>Data Science Portfolio | Andy Cao</title>
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<a href="https://andysucao.github.io/Andy_Portfolio/" class="f3 fw2 hover-white no-underline white-90 dib">
Andy Cao
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<li class="list f5 f4-ns fw4 dib pr3">
<a class="hover-white no-underline white-90" href="https://andysucao.github.io/Andy_Portfolio/about/" title="About page">
About
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<a class="hover-white no-underline white-90" href="https://andysucao.github.io/Andy_Portfolio/contact/" title="Contact page">
Contact
</a>
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<a class="hover-white no-underline white-90" href="https://andysucao.github.io/Andy_Portfolio/post/" title="Projects page">
Projects
</a>
</li>
<li class="list f5 f4-ns fw4 dib pr3">
<a class="hover-white no-underline white-90" href="https://andysucao.github.io/Andy_Portfolio/skills/" title="Useful skills page">
Useful skills
</a>
</li>
</ul>
<a href="https://www.linkedin.com/in/cao/" target="_blank" class="link-transition linkedin link dib z-999 pt3 pt0-l mr1" title="LinkedIn link" rel="noopener" aria-label="follow on LinkedIn——Opens in a new window">
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</nav>
<div class="tc-l pv4 pv6-l ph3 ph4-ns">
<h1 class="f2 f-subheadline-l fw2 white-90 mb0 lh-title">
Data Science Portfolio
</h1>
<h2 class="fw1 f5 f3-l white-80 measure-wide-l center mt3">
Hi! My name is Andy Cao. I am a Ph.D. and a Data Scientist.
</h2>
</div>
</div>
</header>
<main class="pb7" role="main">
<article class="cf ph3 ph5-l pv3 pv4-l f4 tc-l center measure-wide lh-copy mid-gray">
<p>Welcome to my Data Science Portfolio.</p>
<p>My passion is understanding the intersection of data science and machine learning.</p>
</article>
<div class="pa3 pa4-ns w-100 w-70-ns center">
<h1 class="flex-none">
Recent Projects
</h1>
<section class="w-100 mw8">
<div class="relative w-100 mb4">
<article class="bb b--black-10">
<div class="db pv4 ph3 ph0-l no-underline dark-gray">
<div class="flex flex-column flex-row-ns">
<div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-10/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-10-1.png" class="img" alt="image from Project 10: Build a Chatbot with LangChain and Chroma to chat with your own documents">
</a>
</div>
<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-10/" class="color-inherit dim link">
Project 10: Build a Chatbot with LangChain and Chroma to chat with your own documents
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview In this project, we will build a Retrieval-Augmented Generation Chatbot with the help of LangChain that can answer questions from internal documentation and have memory. By using Panel’s chat interface, we will also build a LangChain-powered AI chatbot for our RAG application.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
2. LangChain LangChain is an open-source developer framework for building LLM applications.
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-10/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
</div>
</div>
</div>
</article>
</div>
<div class="relative w-100 mb4">
<article class="bb b--black-10">
<div class="db pv4 ph3 ph0-l no-underline dark-gray">
<div class="flex flex-column flex-row-ns">
<div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-9/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-9-1.png" class="img" alt="image from Project 9: Generative QA with Retrieval-Augmented Generation (RAG) and TruEra Evaluation">
</a>
</div>
<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-9/" class="color-inherit dim link">
Project 9: Generative QA with Retrieval-Augmented Generation (RAG) and TruEra Evaluation
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview In this project, we will build a Generative Question Answering model with Retrieval-Augmented Generation (RAG) with the help of LlamaIndex that can answer questions from internal documentation. We will also evaluate, iterate, and improve the model by using TruLens.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
2. Retrieval-Augmented Generation (RAG) for Question Answering (QA) In the first part of this section, we will discuss the basic RAG pipeline for generative Question Answering from internal documentation.
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-9/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
</div>
</div>
</div>
</article>
</div>
<div class="relative w-100 mb4">
<article class="bb b--black-10">
<div class="db pv4 ph3 ph0-l no-underline dark-gray">
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<div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-8/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-8-1.jpg" class="img" alt="image from Project 8: Machine Translation with Transformers">
</a>
</div>
<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-8/" class="color-inherit dim link">
Project 8: Machine Translation with Transformers
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview In this project, we will build a neural machine translation model with Fairseq Transformer that can translate English into Chinese naturally. The model will be trained and evaluated on the TED2020 En-Zh Bilingual Parallel Corpus.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
2. Machine translation and Transformer 2.1. Brief history of machine translation The figure above illustrates the development of Machine Translation from 1950s to today (source).
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-8/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
</div>
</div>
</div>
</article>
</div>
<div class="relative w-100 mb4">
<article class="bb b--black-10">
<div class="db pv4 ph3 ph0-l no-underline dark-gray">
<div class="flex flex-column flex-row-ns">
<div class="pr3-ns mb4 mb0-ns w-100 w-40-ns">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-7/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-7-1.png" class="img" alt="image from Project 7: Extractive QA with a Fine-Tuned BERT">
</a>
</div>
<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-7/" class="color-inherit dim link">
Project 7: Extractive QA with a Fine-Tuned BERT
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview In this project, we will build a Bidirectional Encoder Representations from Transformers (BERT) based model for a different Natural Language Processing task – Question Answering. The model will be fine-tuned on the Conversational Question Answering Challenge (CoQA) dataset from Stanford University.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
2. Question Answering (QA) Question Answering, particularly Extraction-based Question Answering, is another type of Natural Language Processing task.
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-7/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
</div>
</div>
</div>
</article>
</div>
<div class="relative w-100 mb4">
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-6/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-6-1.jpg" class="img" alt="image from Project 6: Natural Language Inference with BERT and Explainable Artificial Intelligence">
</a>
</div>
<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-6/" class="color-inherit dim link">
Project 6: Natural Language Inference with BERT and Explainable Artificial Intelligence
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview In this project, we will build a Bidirectional Encoder Representations from Transformers (BERT) based model for Natural Language Inference. The performance of the model will be evaluated on the Stanford Natural Language Inference (SNLI) Corpus. To further understand how it works, we will visualize attention mechanism and compare output embedding of BERT using Euclidean distance and Cosine similarity.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-6/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
</div>
</div>
</div>
</article>
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<div class="relative w-100 mb4">
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-5/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-5-1d.jpg" class="img" alt="image from Project 5: Using Autoencoder for Anomaly Detection and searching similar images">
</a>
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<div class="blah w-100 w-60-ns pl3-ns">
<h1 class="f3 fw1 athelas mt0 lh-title">
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-5/" class="color-inherit dim link">
Project 5: Using Autoencoder for Anomaly Detection and searching similar images
</a>
</h1>
<div class="f6 f5-l lh-copy nested-copy-line-height nested-links">
1. Overview Anomaly detection is a technique used for identifying rare items, events or observations, which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior wikipedia.
Anomaly detection has very wide applications, such as Fraud detection in credit card transactions ref, Network Intrusion detection ref, and Cancer cell detection ref.
A wide spectrum of techniques have been proposed for anomaly detection, some of the popular methods are: Density-based techniques (e.
</div>
<a href="https://andysucao.github.io/Andy_Portfolio/post/project-5/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
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</article>
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-4/" class="db grow">
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Project 4: Image Classification and Explainable Artificial Intelligence
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1. Project Overview In this project, we will build a model for image classification and understand how it works.
In the first part, we will develop a convolutional neural network (CNN) model for food image classification. We will also apply t-distributed Stochastic Neighbor Embedding (t-SNE) technique on the output of different layers to visualize learned visual representations of the CNN model.
In order to understand how the model works, we will employ four popular Explainable AI approaches in the second part, including (1) Saliency map, (2) Smooth gradient, (3) Lime package, and (4) Integrated gradients.
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-4/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-3/" class="db grow">
<img src="https://andysucao.github.io/Andy_Portfolio/images/projects-3-1.jpg" class="img" alt="image from Project 3: Understanding Ridesharing Demands in New York City">
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Project 3: Understanding Ridesharing Demands in New York City
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1. Overview In this project, we will focus on New York City’s Ridesharing Trips Data Set and try to answer three interesting questions:
(1) How do the residents of New York City use Ridesharing?
(2) What usage patterns can we see?
(3) Can we predict usage?
To answer these questions, first we will clean up the trip data and integrate them with weather data and taxi zone geodata. Then we will conduct Exploratory Data Analysis (EDA) and Statistical Tests to find Temporal and Spatial Patterns, as well as weather effects and tipping behaviors.
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-3/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
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Project 2: Predicitng Customer Churn for a Mobile Phone Carrier
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1. Overview In this project, we will build a series of models to predict the probability of Customer Churn for a Mobile Phone Carrier.
In the first part, we will conduct Exploratory Data Analysis (EDA). Here the Synthetic Minority Oversampling Technique (SMOTE) method is employed to address the imbalanced classification problem. Then we will develop customer churn prediction models based on (1) Logistic Regression, (2) Decision Tree, (3) Random Forest, (4) AdaBoost, (5) Gradient Boosting Decision Trees (GBDT), and (6) Neural Network.
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-2/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
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Project 1: Predict Real Estate Prices in Beijing
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1. Overview In this project, we will build a linear regression model to help people better understand the factors that can affect the price of a house in Beijing.
The main sections of this article are: (1) Exploratory Data Analysis (EDA), (2) Developing Linear Regression model for predicting real estate price, and (3) Use that model to make predictions.
The Python Notebook containing the complete model development process and the data used in this project can be found at Google Drive.
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<a href="https://andysucao.github.io/Andy_Portfolio/post/project-1/" class="ba b--moon-gray bg-light-gray br2 color-inherit dib f7 hover-bg-moon-gray link mt2 ph2 pv1">read more</a>
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